FastTreeTweedieTrainer Class
Definition
Important
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The IEstimator<TTransformer> for training a decision tree regression model using Tweedie loss function. This trainer is a generalization of Poisson, compound Poisson, and gamma regression.
public sealed class FastTreeTweedieTrainer : Microsoft.ML.Trainers.FastTree.BoostingFastTreeTrainerBase<Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options,Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.FastTree.FastTreeTweedieModelParameters>,Microsoft.ML.Trainers.FastTree.FastTreeTweedieModelParameters>type FastTreeTweedieTrainer = class
inherit BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Options, RegressionPredictionTransformer<FastTreeTweedieModelParameters>, FastTreeTweedieModelParameters>Public NotInheritable Class FastTreeTweedieTrainer
Inherits BoostingFastTreeTrainerBase(Of FastTreeTweedieTrainer.Options, RegressionPredictionTransformer(Of FastTreeTweedieModelParameters), FastTreeTweedieModelParameters)- Inheritance
Remarks
To create this trainer, use FastTreeTweedie or FastTreeTweedie(Options).
Input and Output Columns
The input label column data must be Single. The input features column data must be a known-sized vector of Single.
This trainer outputs the following columns:
| Output Column Name | Column Type | Description |
|---|---|---|
Score |
Single | The unbounded score that was predicted by the model. |
Trainer Characteristics
| Machine learning task | Regression |
| Is normalization required? | No |
| Is caching required? | No |
| Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
| Exportable to ONNX | Yes |
Training Algorithm Details
The Tweedie boosting model follows the mathematics established in Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models from Yang, Quan, and Zou. For an introduction to Gradient Boosting, and more information, see: Wikipedia: Gradient boosting(Gradient tree boosting) or Greedy function approximation: A gradient boosting machine.
Check the See Also section for links to usage examples.
Fields
| FeatureColumn |
The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
| GroupIdColumn |
The optional groupID column that the ranking trainers expects. (Inherited from TrainerEstimatorBaseWithGroupId<TTransformer,TModel>) |
| LabelColumn |
The label column that the trainer expects. Can be |
| WeightColumn |
The weight column that the trainer expects. Can be |
Properties
| Info | (Inherited from FastTreeTrainerBase<TOptions,TTransformer,TModel>) |
Methods
| Fit(IDataView) |
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
| Fit(IDataView, IDataView) |
Trains a FastTreeTweedieTrainer using both training and validation data, returns a RegressionPredictionTransformer<TModel>. |
| GetOutputSchema(SchemaShape) | (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |